
# 2: Fundamentals of Modeling

$$\newcommand{\vecs}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} }$$

$$\newcommand{\vecd}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash {#1}}}$$

• 2.1: Models in Science and Engineering
Science is an endeavor to try to understand the world around us by discovering fundamental laws that describe how it works. Such laws include Newton’s law of motion, the ideal gas law, Ohm’s law in electrical circuits, the conservation law of energy, and so on, some of which you may have learned already.
• 2.2: How to Create a Model
There are a number of approaches for scientiﬁc model building. My favorite way of classifying various kinds of modeling approaches is to put them into the following two major families:
• 2.3: Modeling Complex Systems
The challenge in developing a model becomes particularly tough when it comes to the modeling of complex systems, because their unique properties (networks, nonlinearity, emergence, self-organization, etc.) are not what we are familiar with. We usually think about things on a single scale in a step-by-step, linear chain of reasoning, in which causes and effects are clearly distinguished and discussed sequentially. But this approach is not suitable for understanding complex systems where a massive
• 2.4: What Are Good Models?
Simplicity of a model is really the key essence of what modeling is all about. The main reason why we want to build a model is that we want to have a shorter, simpler description of reality.
• 2.5: A Historical Perspective
Humans have been creating descriptive models and some conceptual rule-based models since ancient times. More quantitative modeling approaches arose as more advanced mathematical tools became available. In the descriptive modeling family, descriptive statistics is among such quantitative modeling approaches. In the rule-based modeling family, dynamical equations (e.g., differential equations) began to be used to quantitatively formulate theories that had remained at conceptual levels before.